def test_classify_with_ds_diff_sizes():
query = np.ones((10, 2))
predictions = np.ones((5, 3))
des_mi = DESMI()
with pytest.raises(ValueError):
des_mi.classify_with_ds(query, predictions)
def test_classify_with_ds_single_sample():
query = np.ones(2)
# simulated predictions of the pool of classifiers
predictions = np.array([0, 1, 0])
desmi_test = DESMI(DFP=True)
DFP_mask = np.ones((1, 3))
desmi_test.estimate_competence = MagicMock(return_value=(np.ones((1, 3))))
desmi_test.select = MagicMock(return_value=np.array([[0, 2]]))
result = desmi_test.classify_with_ds(query, predictions, DFP_mask=DFP_mask)
assert np.allclose(result, 0)
def test_classify_with_ds_batch_samples():
n_samples = 10
# simulated predictions of the pool of classifiers
predictions = np.tile(np.array([0, 1, 0]), (n_samples, 1))
desmi_test = DESMI()
desmi_test.n_classes_ = 2
desmi_test.estimate_competence = MagicMock(
return_value=(np.ones((n_samples, 3))))
desmi_test.select = MagicMock(
return_value=np.tile(np.array([[0, 2]]), (n_samples, 1)))
result = desmi_test.classify_with_ds(predictions)
assert np.allclose(result, np.zeros(10))
def test_classify_with_ds_batch_samples():
n_samples = 10
# Passing 10 samples for classification automatically
query = np.ones((n_samples, 2))
pool_classifiers = create_pool_classifiers()
# simulated predictions of the pool of classifiers
predictions = np.tile(np.array([0, 1, 0]), (n_samples, 1))
desmi_test = DESMI(pool_classifiers)
desmi_test.estimate_competence = MagicMock(return_value=(np.ones((n_samples, 3))))
desmi_test.select = MagicMock(return_value=np.tile(np.array([[0, 2]]), (n_samples, 1)))
result = desmi_test.classify_with_ds(query, predictions)
assert np.allclose(result, np.zeros(10))